Overview

Dataset statistics

Number of variables41
Number of observations70372
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.0 MiB
Average record size in memory328.0 B

Variable types

BOOL29
NUM9
CAT3

Warnings

order_dt has a high cardinality: 2160 distinct values High cardinality
first_order_dt has a high cardinality: 2931 distinct values High cardinality
first_cont_dt has a high cardinality: 1398 distinct values High cardinality
tot_contribution_paid_amt is highly skewed (γ1 = 69.13668969) Skewed
Lifetime Giving is highly skewed (γ1 = 30.32802605) Skewed
tot_ticket_paid_amt has 34085 (48.4%) zeros Zeros
tot_contribution_paid_amt has 68563 (97.4%) zeros Zeros
Prelim Capacity has 30829 (43.8%) zeros Zeros
ltv_tkt_value has 21484 (30.5%) zeros Zeros
Lifetime Giving has 58829 (83.6%) zeros Zeros
days_to_donation has 1248 (1.8%) zeros Zeros
rolling_tkt_sum has 26312 (37.4%) zeros Zeros

Reproduction

Analysis started2020-09-08 17:03:00.404623
Analysis finished2020-09-08 17:04:58.844874
Duration1 minute and 58.44 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

owner_no
Real number (ℝ≥0)

Distinct38522
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2022525.081
Minimum111
Maximum2419301
Zeros0
Zeros (%)0.0%
Memory size549.8 KiB
2020-09-08T13:04:59.183691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile397258.05
Q11928112
median2370216.5
Q32391456
95-th percentile2413551.9
Maximum2419301
Range2419190
Interquartile range (IQR)463344

Descriptive statistics

Standard deviation629695.9448
Coefficient of variation (CV)0.3113414765
Kurtosis2.187826958
Mean2022525.081
Median Absolute Deviation (MAD)40994.5
Skewness-1.835938465
Sum1.42329135e+11
Variance3.965169829e+11
MonotocityIncreasing
2020-09-08T13:04:59.832091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2335299660.1%
 
1485790640.1%
 
275098480.1%
 
2289267470.1%
 
2373627470.1%
 
2140595460.1%
 
2133270440.1%
 
2375331430.1%
 
2388599430.1%
 
2387926420.1%
 
Other values (38512)6988299.3%
 
ValueCountFrequency (%) 
1113< 0.1%
 
2581< 0.1%
 
2622< 0.1%
 
2675< 0.1%
 
8533< 0.1%
 
ValueCountFrequency (%) 
24193011< 0.1%
 
24192781< 0.1%
 
24192481< 0.1%
 
24191261< 0.1%
 
24190111< 0.1%
 

order_dt
Categorical

HIGH CARDINALITY

Distinct2160
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
2017-08-23
 
1301
2019-08-14
 
1024
2015-09-02
 
919
2016-09-07
 
822
2014-09-03
 
796
Other values (2155)
65510 
ValueCountFrequency (%) 
2017-08-2313011.8%
 
2019-08-1410241.5%
 
2015-09-029191.3%
 
2016-09-078221.2%
 
2014-09-037961.1%
 
2018-08-297131.0%
 
2016-09-234230.6%
 
2017-09-234200.6%
 
2017-09-193870.5%
 
2016-09-273760.5%
 
Other values (2150)6319189.8%
 
2020-09-08T13:05:00.422753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique142 ?
Unique (%)0.2%
2020-09-08T13:05:01.108873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

tot_ticket_paid_amt
Real number (ℝ≥0)

ZEROS

Distinct1159
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.3262916
Minimum0
Maximum5400
Zeros34085
Zeros (%)48.4%
Memory size549.8 KiB
2020-09-08T13:05:01.544634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q3156.6755609
95-th percentile432
Maximum5400
Range5400
Interquartile range (IQR)156.6755609

Descriptive statistics

Standard deviation197.7355773
Coefficient of variation (CV)1.842377802
Kurtosis64.08996281
Mean107.3262916
Median Absolute Deviation (MAD)20
Skewness5.40552422
Sum7552765.793
Variance39099.35852
MonotocityNot monotonic
2020-09-08T13:05:01.986242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03408548.4%
 
4010321.5%
 
209531.4%
 
1987931.1%
 
387611.1%
 
1387141.0%
 
2386951.0%
 
606751.0%
 
1006590.9%
 
506330.9%
 
Other values (1149)2937241.7%
 
ValueCountFrequency (%) 
03408548.4%
 
11< 0.1%
 
24< 0.1%
 
31< 0.1%
 
414< 0.1%
 
ValueCountFrequency (%) 
54001< 0.1%
 
51801< 0.1%
 
45881< 0.1%
 
44141< 0.1%
 
42921< 0.1%
 

tot_contribution_paid_amt
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct131
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.020798045
Minimum0
Maximum5000
Zeros68563
Zeros (%)97.4%
Memory size549.8 KiB
2020-09-08T13:05:02.916260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5000
Range5000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation42.71554863
Coefficient of variation (CV)21.1379602
Kurtosis6497.137924
Mean2.020798045
Median Absolute Deviation (MAD)0
Skewness69.13668969
Sum142207.6
Variance1824.618095
MonotocityNot monotonic
2020-09-08T13:05:03.527428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
06856397.4%
 
1003340.5%
 
102170.3%
 
251830.3%
 
501280.2%
 
201250.2%
 
5970.1%
 
40760.1%
 
2470.1%
 
25032< 0.1%
 
Other values (121)5700.8%
 
ValueCountFrequency (%) 
06856397.4%
 
121< 0.1%
 
1.51< 0.1%
 
2470.1%
 
2.23< 0.1%
 
ValueCountFrequency (%) 
50002< 0.1%
 
25005< 0.1%
 
22501< 0.1%
 
20002< 0.1%
 
15001< 0.1%
 

first_order_dt
Categorical

HIGH CARDINALITY

Distinct2931
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
2017-08-23
 
824
2015-09-02
 
672
2014-09-03
 
635
2016-09-23
 
462
2014-09-23
 
446
Other values (2926)
67333 
ValueCountFrequency (%) 
2017-08-238241.2%
 
2015-09-026721.0%
 
2014-09-036350.9%
 
2016-09-234620.7%
 
2014-09-234460.6%
 
2014-09-264240.6%
 
2016-09-154160.6%
 
2016-09-074100.6%
 
2014-09-224070.6%
 
2015-09-283780.5%
 
Other values (2921)6529892.8%
 
2020-09-08T13:05:04.305117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique281 ?
Unique (%)0.4%
2020-09-08T13:05:04.838866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

first_cont_dt
Categorical

HIGH CARDINALITY

Distinct1398
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
1900-01-01
58782 
2018-08-06
 
490
2020-03-26
 
226
2020-03-30
 
148
2020-03-25
 
100
Other values (1393)
10626 
ValueCountFrequency (%) 
1900-01-015878283.5%
 
2018-08-064900.7%
 
2020-03-262260.3%
 
2020-03-301480.2%
 
2020-03-251000.1%
 
2020-04-24890.1%
 
2020-05-05830.1%
 
2020-03-27810.1%
 
2016-03-30660.1%
 
2016-09-07660.1%
 
Other values (1388)1024114.6%
 
2020-09-08T13:05:05.234638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique298 ?
Unique (%)0.4%
2020-09-08T13:05:05.793320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

geo_area_desc
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.674103337
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size549.8 KiB
2020-09-08T13:05:06.085153image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.497853338
Coefficient of variation (CV)0.8947197612
Kurtosis5.987047599
Mean1.674103337
Median Absolute Deviation (MAD)0
Skewness2.620774255
Sum117810
Variance2.243564621
MonotocityNot monotonic
2020-09-08T13:05:06.394974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
15116872.7%
 
21027714.6%
 
734734.9%
 
324283.5%
 
515032.1%
 
410801.5%
 
64430.6%
 
ValueCountFrequency (%) 
15116872.7%
 
21027714.6%
 
324283.5%
 
410801.5%
 
515032.1%
 
ValueCountFrequency (%) 
734734.9%
 
64430.6%
 
515032.1%
 
410801.5%
 
324283.5%
 

Prelim Capacity
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.910319445
Minimum0
Maximum10
Zeros30829
Zeros (%)43.8%
Memory size549.8 KiB
2020-09-08T13:05:06.748770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.946138595
Coefficient of variation (CV)1.018750345
Kurtosis-1.09127187
Mean1.910319445
Median Absolute Deviation (MAD)2
Skewness0.4400920866
Sum134433
Variance3.787455429
MonotocityNot monotonic
2020-09-08T13:05:07.044602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
03082943.8%
 
41162216.5%
 
31104315.7%
 
266729.5%
 
552717.5%
 
129974.3%
 
616112.3%
 
71810.3%
 
81300.2%
 
913< 0.1%
 
ValueCountFrequency (%) 
03082943.8%
 
129974.3%
 
266729.5%
 
31104315.7%
 
41162216.5%
 
ValueCountFrequency (%) 
103< 0.1%
 
913< 0.1%
 
81300.2%
 
71810.3%
 
616112.3%
 

ltv_tkt_value
Real number (ℝ≥0)

ZEROS

Distinct2560
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean793.0981255
Minimum0
Maximum42312
Zeros21484
Zeros (%)30.5%
Memory size549.8 KiB
2020-09-08T13:05:07.497061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median149
Q3536
95-th percentile3740
Maximum42312
Range42312
Interquartile range (IQR)536

Descriptive statistics

Standard deviation2425.538425
Coefficient of variation (CV)3.058308104
Kurtosis94.91915165
Mean793.0981255
Median Absolute Deviation (MAD)149
Skewness8.256546927
Sum55811901.29
Variance5883236.65
MonotocityNot monotonic
2020-09-08T13:05:08.068866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02148430.5%
 
407821.1%
 
207581.1%
 
605640.8%
 
385000.7%
 
1384920.7%
 
2384760.7%
 
1984640.7%
 
784520.6%
 
1184300.6%
 
Other values (2550)4397062.5%
 
ValueCountFrequency (%) 
02148430.5%
 
22< 0.1%
 
413< 0.1%
 
57< 0.1%
 
61< 0.1%
 
ValueCountFrequency (%) 
4231231< 0.1%
 
3327133< 0.1%
 
30819640.1%
 
2999731< 0.1%
 
2113117< 0.1%
 

Lifetime Giving
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct584
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4568.75912
Minimum0
Maximum3417602
Zeros58829
Zeros (%)83.6%
Memory size549.8 KiB
2020-09-08T13:05:08.745051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile490
Maximum3417602
Range3417602
Interquartile range (IQR)0

Descriptive statistics

Standard deviation106920.769
Coefficient of variation (CV)23.40258399
Kurtosis951.2318377
Mean4568.75912
Median Absolute Deviation (MAD)0
Skewness30.32802605
Sum321512716.8
Variance1.143205085e+10
MonotocityNot monotonic
2020-09-08T13:05:09.393677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05882983.6%
 
1007921.1%
 
256731.0%
 
104880.7%
 
504650.7%
 
202660.4%
 
2002640.4%
 
52040.3%
 
2501930.3%
 
401610.2%
 
Other values (574)803711.4%
 
ValueCountFrequency (%) 
05882983.6%
 
1380.1%
 
1.252< 0.1%
 
1.374< 0.1%
 
2920.1%
 
ValueCountFrequency (%) 
3417602640.1%
 
20610423< 0.1%
 
104230131< 0.1%
 
461460.0815< 0.1%
 
35760116< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
69057 
1
 
1315
ValueCountFrequency (%) 
06905798.1%
 
113151.9%
 
2020-09-08T13:05:09.768460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

days_to_donation
Real number (ℝ)

ZEROS

Distinct1172
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.23992497
Minimum-100
Maximum3747
Zeros1248
Zeros (%)1.8%
Memory size549.8 KiB
2020-09-08T13:05:10.066299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-100
Q1-100
median-100
Q3-100
95-th percentile1108
Maximum3747
Range3847
Interquartile range (IQR)0

Descriptive statistics

Standard deviation490.9563657
Coefficient of variation (CV)9.398106256
Kurtosis17.90792151
Mean52.23992497
Median Absolute Deviation (MAD)0
Skewness4.0617276
Sum3676228
Variance241038.153
MonotocityNot monotonic
2020-09-08T13:05:10.981831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-1005878283.5%
 
012481.8%
 
-17871.1%
 
1506660.1%
 
177640.1%
 
1580.1%
 
153520.1%
 
604500.1%
 
109490.1%
 
807480.1%
 
Other values (1162)916813.0%
 
ValueCountFrequency (%) 
-1005878283.5%
 
-17871.1%
 
012481.8%
 
1580.1%
 
214< 0.1%
 
ValueCountFrequency (%) 
374725< 0.1%
 
37236< 0.1%
 
37167< 0.1%
 
36941< 0.1%
 
368313< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
69124 
1
 
1248
ValueCountFrequency (%) 
06912498.2%
 
112481.8%
 
2020-09-08T13:05:11.470061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
60817 
1
9555 
ValueCountFrequency (%) 
06081786.4%
 
1955513.6%
 
2020-09-08T13:05:11.830431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

rolling_tkt_sum
Real number (ℝ≥0)

ZEROS

Distinct4868
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean806.984696
Minimum0
Maximum153692
Zeros26312
Zeros (%)37.4%
Memory size549.8 KiB
2020-09-08T13:05:12.438118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median80
Q3310
95-th percentile3048.35
Maximum153692
Range153692
Interquartile range (IQR)310

Descriptive statistics

Standard deviation4194.586959
Coefficient of variation (CV)5.197851929
Kurtosis426.3247918
Mean806.984696
Median Absolute Deviation (MAD)80
Skewness17.04718958
Sum56789127.03
Variance17594559.75
MonotocityNot monotonic
2020-09-08T13:05:13.614117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02631237.4%
 
408521.2%
 
207541.1%
 
387151.0%
 
1386580.9%
 
2386210.9%
 
1986010.9%
 
785640.8%
 
605500.8%
 
2584620.7%
 
Other values (4858)3828354.4%
 
ValueCountFrequency (%) 
02631237.4%
 
23< 0.1%
 
411< 0.1%
 
57< 0.1%
 
61< 0.1%
 
ValueCountFrequency (%) 
1536922< 0.1%
 
1529201< 0.1%
 
1403202< 0.1%
 
1400804< 0.1%
 
1399801< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
67715 
1
 
2657
ValueCountFrequency (%) 
06771596.2%
 
126573.8%
 
2020-09-08T13:05:14.328732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
70169 
1
 
203
ValueCountFrequency (%) 
07016999.7%
 
12030.3%
 
2020-09-08T13:05:14.620562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
70178 
1
 
194
ValueCountFrequency (%) 
07017899.7%
 
11940.3%
 
2020-09-08T13:05:14.930384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
63212 
1
7160 
ValueCountFrequency (%) 
06321289.8%
 
1716010.2%
 
2020-09-08T13:05:15.199231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
68361 
1
 
2011
ValueCountFrequency (%) 
06836197.1%
 
120112.9%
 
2020-09-08T13:05:15.516049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
70358 
1
 
14
ValueCountFrequency (%) 
070358> 99.9%
 
114< 0.1%
 
2020-09-08T13:05:15.800884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
64861 
1
 
5511
ValueCountFrequency (%) 
06486192.2%
 
155117.8%
 
2020-09-08T13:05:16.072735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
70039 
1
 
333
ValueCountFrequency (%) 
07003999.5%
 
13330.5%
 
2020-09-08T13:05:16.396073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
62036 
1
8336 
ValueCountFrequency (%) 
06203688.2%
 
1833611.8%
 
2020-09-08T13:05:16.621942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
1
35256 
0
35116 
ValueCountFrequency (%) 
13525650.1%
 
03511649.9%
 
2020-09-08T13:05:16.896786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
61875 
1
8497 
ValueCountFrequency (%) 
06187587.9%
 
1849712.1%
 
2020-09-08T13:05:17.125655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
70371 
1
 
1
ValueCountFrequency (%) 
070371> 99.9%
 
11< 0.1%
 
2020-09-08T13:05:17.361122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
69808 
1
 
564
ValueCountFrequency (%) 
06980899.2%
 
15640.8%
 
2020-09-08T13:05:17.609978image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
59247 
1
11125 
ValueCountFrequency (%) 
05924784.2%
 
11112515.8%
 
2020-09-08T13:05:17.826855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
1
41201 
0
29171 
ValueCountFrequency (%) 
14120158.5%
 
02917141.5%
 
2020-09-08T13:05:18.068714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
52118 
1
18254 
ValueCountFrequency (%) 
05211874.1%
 
11825425.9%
 
2020-09-08T13:05:18.328575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
1
43240 
0
27132 
ValueCountFrequency (%) 
14324061.4%
 
02713238.6%
 
2020-09-08T13:05:18.560450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
69424 
1
 
948
ValueCountFrequency (%) 
06942498.7%
 
19481.3%
 
2020-09-08T13:05:18.795308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
59870 
1
10502 
ValueCountFrequency (%) 
05987085.1%
 
11050214.9%
 
2020-09-08T13:05:19.058158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
54426 
1
15946 
ValueCountFrequency (%) 
05442677.3%
 
11594622.7%
 
2020-09-08T13:05:19.304017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
1
38198 
0
32174 
ValueCountFrequency (%) 
13819854.3%
 
03217445.7%
 
2020-09-08T13:05:19.580858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
68456 
1
 
1916
ValueCountFrequency (%) 
06845697.3%
 
119162.7%
 
2020-09-08T13:05:19.828715image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
48723 
1
21649 
ValueCountFrequency (%) 
04872369.2%
 
12164930.8%
 
2020-09-08T13:05:19.965638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
68507 
1
 
1865
ValueCountFrequency (%) 
06850797.3%
 
118652.7%
 
2020-09-08T13:05:20.114554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
62351 
1
8021 
ValueCountFrequency (%) 
06235188.6%
 
1802111.4%
 
2020-09-08T13:05:20.298446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size549.8 KiB
0
66278 
1
 
4094
ValueCountFrequency (%) 
06627894.2%
 
140945.8%
 
2020-09-08T13:05:20.463351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Interactions

2020-09-08T13:03:58.420867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:03:59.470663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:00.385588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:00.968264image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:01.428001image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:02.012746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:02.598670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:03.317667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:03.771475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:04.242232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:04.636803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:05.047459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:05.511452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:05.966511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:06.629177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:07.032535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:07.538243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:07.984451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:08.561121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:08.952368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:09.429031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:09.924743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:10.466433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:10.836221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:11.283361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:11.782593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:12.212347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:12.652096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:13.081850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:13.532374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:13.993583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:14.669474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:15.155197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:15.619513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:16.199725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:16.734418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:17.361852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:17.963021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:18.504095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:18.942845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:19.430576image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:19.949302image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:20.444017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:20.899772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:21.613483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:22.417407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:22.880777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:23.429216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:23.868963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:24.365968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:24.803722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:25.147442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:25.632378image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:26.079643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:26.547479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:26.987861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:27.464598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:27.995870image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:28.803529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:29.587590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:30.337455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:31.170138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:32.015665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:32.997101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:33.771462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:34.498131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:35.153750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:36.259108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:37.034623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:37.564831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:38.010578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:38.554482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:39.363583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:39.833468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:40.344521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:40.864516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:41.431449image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:41.922224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:42.462396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:42.929538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:43.553454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-08T13:05:20.883620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-08T13:05:22.605405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-08T13:05:24.338885image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-08T13:05:26.160821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-08T13:04:45.177439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-08T13:04:56.490210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

owner_noorder_dttot_ticket_paid_amttot_contribution_paid_amtfirst_order_dtfirst_cont_dtgeo_area_descPrelim Capacityltv_tkt_valueLifetime Givingprospect_boarddays_to_donationfirst_cont_orderfirst_cont_afterrolling_tkt_sumchannel_desc_3rd Partychannel_desc_At the Performancechannel_desc_Chatchannel_desc_Default Channelchannel_desc_Emailchannel_desc_Faxchannel_desc_Internal Requestchannel_desc_Mailchannel_desc_Mobilechannel_desc_Onlinechannel_desc_Phonechannel_desc_Telefundingchannel_desc_Walk UpMOS_desc_ExternalMOS_desc_InternalMOS_desc_Ticketingdelivery_desc_Digitaldelivery_desc_Do Not Print Ticketsdelivery_desc_Maildelivery_desc_Will Callfacility_desc_Academy of Musicfacility_desc_Fundraiserfacility_desc_Independence Mallfacility_desc_Otherfacility_desc_Perelmanfacility_desc_Small venue
01112015-10-07138.00.02015-10-071900-01-0115366.00.00-10000138.000010000000001000001100000
11112016-09-16158.00.02015-10-071900-01-0115366.00.00-10000296.000000000001000010001100000
21112018-09-2170.00.02015-10-071900-01-0115366.00.00-10000366.010000000000001000001100000
32582014-09-28169.00.02014-09-281900-01-0156169.00.00-10000169.000010000000001000001100000
42622014-10-0245.00.02014-10-021900-01-012490.00.00-10000135.000000000001000010001100000
52622015-03-1745.00.02014-10-021900-01-012490.00.00-10000270.000000000001000010010100000
62672015-04-160.00.02015-04-161900-01-01161778.00.00-100000.000000010000000010001100000
72672016-04-13298.00.02015-04-161900-01-01161778.00.00-10000298.000000000010000100010100000
82672016-05-030.00.02015-04-161900-01-01161778.00.00-100000.000000010000000010001010000
92672018-05-091400.00.02015-04-161900-01-01161778.00.00-100001698.000000000010000101000000001

Last rows

owner_noorder_dttot_ticket_paid_amttot_contribution_paid_amtfirst_order_dtfirst_cont_dtgeo_area_descPrelim Capacityltv_tkt_valueLifetime Givingprospect_boarddays_to_donationfirst_cont_orderfirst_cont_afterrolling_tkt_sumchannel_desc_3rd Partychannel_desc_At the Performancechannel_desc_Chatchannel_desc_Default Channelchannel_desc_Emailchannel_desc_Faxchannel_desc_Internal Requestchannel_desc_Mailchannel_desc_Mobilechannel_desc_Onlinechannel_desc_Phonechannel_desc_Telefundingchannel_desc_Walk UpMOS_desc_ExternalMOS_desc_InternalMOS_desc_Ticketingdelivery_desc_Digitaldelivery_desc_Do Not Print Ticketsdelivery_desc_Maildelivery_desc_Will Callfacility_desc_Academy of Musicfacility_desc_Fundraiserfacility_desc_Independence Mallfacility_desc_Otherfacility_desc_Perelmanfacility_desc_Small venue
7036224188662016-02-0890.00.02012-10-042020-04-207190.025.0027550190.000000000010000101000100000
7036324188662016-02-120.00.02012-10-042020-04-207190.025.002755010.000000010000000010001010000
7036424189312018-04-25240.00.02018-04-252020-05-0525240.0100.0074101240.000000000010000101000100000
7036524189992016-10-010.020.02016-10-012016-10-0113124.020.000100.000000000010000101000001000
7036624189992017-02-13124.00.02016-10-012016-10-0113124.020.00010124.000000000010000101000100000
7036724190112018-09-070.025.02018-09-072018-09-07250.050.000100.000000000010000101000001000
7036824191262019-09-190.00.02019-09-191900-01-0110600.00.00-100000.000000010000000010001100000
7036924192482017-09-20440.00.02017-09-202019-12-1124440.0100.0081201440.000000000001000011000100000
7037024192782015-04-13395.00.02011-04-032003-03-2613752.075.00-100395.000010000000001000010100000
7037124193012018-01-18120.00.02018-01-181900-01-0110120.00.00-10000120.000000000010000100010000010